* L2EP, HEI, 13 Rue de Toul, 59800, Lille Cedex France ** GB SOLAR, rue de la Clef PARIS Cedex France

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1 Hybridization of electrical energy storage for intelligent integration of photovoltaics in electric networks Azeddine Houari*, Dhaker Abbes*, Antoine Labrunie**, Benoit Robyns* Acknowledgements * L2EP, HEI, 3 Rue de Toul, 598, Lille Cedex France ** GB SOLAR, rue de la Clef PARIS Cedex France dhaker.abbes@hei.fr Authors want to thank the pole MEDEE in region Nord Pas De Calais, France for its financial support. Keywords «Photovoltaic System», «Hybrid Storage», «Energy Management», «Batteries Lifespan», «Rainflow Method». Abstract In this paper, a photovoltaic system with super capacitors and batteries hybrid storage connected to the grid has been studied. A smart supervision algorithm based on fuzzy logic has been successfully developed. In addition, contribution of storage hybridization on batteries longevity has been proved using rain-flow cumulative damage method. Introduction Energy productions from renewable energy sources such as photovoltaic generators are characterized by uncertainty and intermittence. They are greatly influenced by meteorological conditions. Thus, to ensure good stability of the network, it is necessary to store part of the produced energy. In fact, there are several methods of storage: potential form (STEP), kinetic (flywheel), hydrogen, in an electrochemical battery (lead, lithium) or a super-capacitor [-3]. Currently, there are several companies that sell storage solutions for network support or future integration into smart grids, such as: Li-ion batteries containers (Saft, Mitsubishi Heavy Industries), Batteries Sodium-Sulfur (NaS) (NGK), Hydrogen production and storage systems (CETH2 and McPhy), Flywheel systems (Vycon, Beacon Power), Supercapacitors containers (Maxwell). However, It is rare at the moment a system dedicated to electricity network and combining these various technologies; these systems staying at the moment in development. From an academic perspective, there are several works that are interested in coupling batteries and supercapacitors in particular in embedded applications [2].We also find applications for photovoltaic systems [4-6]. However, works that combine other types of storage are rare [7-9]. In this paper, we have studied a photovoltaic system with super capacitors and batteries hybrid storage. We focussed on optimal system energy management using fuzzy logic and we proved the benefit of hybridization on batteries lifespan using rain-flow method.. Studied Photovoltaic system description Figure presents a synopsis of the photovoltaic power plant studied and the critical role of the supervisor in the management system. Storage station is based on combination of two complementary technologies: a source of power storage (super-capacitors) and a source of energy storage (lithium batteries with high specific energy). The main purpose of adding a source of power storage is to increase the lifetime of the energy storage one.

2 2. Fuzzy logic supervisor Energy management for electrical energy storage is an important topic of research, particularly in multisource systems that combine random and difficult sources to predict such as photovoltaics renewable energy. In fact, in addition to the need for real-time management incorporating unpredictability production, management have to ensure several objectives at once (constraints and network services, storage levels and availability, lifespan and aging, etc.) [].In our case, flow management is intend to satisfy Day- production planning, to participate in frequency support and to protect storage elements monitoring their state of charge. Supervisor construction is based on a multi-step methodology with fuzzy logic as described in []. Fuzzy logic is suitable to manage complex hybrid energy sources with multi-objective supervision and many scenarios to test, as it is difficult to obtain and use precise models, or to predict behavior of sun, as well as load consumptions. a) Architecture b) Management structure Fig.. Block diagram of the studied system A. Operating Specifications Operating specifications for fuzzy logic based supervisor are summarized in Table I. Table I: Supervisor operating specifications Objectives Constraints Actions. Meet a production schedule ensuring injected power smoothness.. Participate in frequency support (primary and secondary Supports).. Improve storage elements life by optimizing their management..pv production intermittency (amplitude and duration variations).. Storage elements size. Primary support in less than 5sec for 5 minutes. Response in less than 5 minutes for 3 minutes in case of secondary support.. The error on the production schedule : - Error margin on the production schedule : Less than % in hourly energy of the power plant compared with production program. Beyond this constraint, there is a risk that 2. Two power storage references: - long-term (batteries), - short-term (power source: super-capacitors).. Degradation factor of photovoltaic production.

3 the photovoltaic producer loses his hour of production. - In case of excess or lack of energy injected according to that suited to Day- with the network administrator, the hour of production is lost. B. Supervisor structure and inputs outputs determination The block diagram of the fuzzy supervisor is shown in Figure 2. Fig.2. Block diagram of the fuzzy supervisor ΔP : Difference between planned and actual photovoltaic power production. ΔP bat and ΔP SdP : respectively batteries and super-capacitors powers after filter separation. SOC bat and SOC SdP : respectively batteries and super-capacitors State Of Charge. Δf : Frequency variation with Δf C_t for short term variations and Δf L_t for long term ones. P bat_ref : Batteries reference power. It is the sum of two sub-outputs, initial reference P bat_ref and secondary support power frequency Δp bat_ref. P SdP_ref : Storage power source (super-capacitors) reference. It is the sum of two sub-outputs, initial reference P SdP_ref and primary support power frequency Δp SdP_ref. G i=:5 : Normalization gains. HPF: high pass filter (first-order filter). K pv : PV production degradation factor. C. Operating modes (functional graphs) Figure 3 summarizes the functional graph of the system and the various sub-graphs detailing the principle of the three main modes of operation: Normal or main mode (N): The SOC is medium or nominal (SOC M ) and the first aim of this mode is to meet the production program planned at day-. The storage system has to fill the gap between the 3

4 instantaneous power and photovoltaic production planned in day- while maintaining the functionality of power smoothing and frequency support. Overcharge mode (N2): This mode is dedicated to protecting storage system against the harmful effect of an overcharge on their lifespan. The principle is to minimize photovoltaic generation to discharge the storage elements until their nominal value. Deep discharge mode (N3): This mode is dedicated to protecting storage system against the harmful effects of deep discharge on their lifespan. The principle is to guarantee storage capacity by well preparing storage elements to production program. Ideally charge storage organs until their nominal value. Charge may be provided by photovoltaic production on the same day before beginning the production program (e.g. in a summer day) or directly from recharging via the grid. D. Memberships functions This step consists on defining the numerical values of fuzzy inputs (fuzzification) considered in figure 2 in function of objectives, constraints and system specifications (figure 4). Linguistic fuzzy states notations: L: Low, M: Medium, H: High, Z: Zero, P: positive (positive), N: negative. E. Fuzzy Modes (operational graphs) This step consists on translating and developing the functional graphs with the membership functions. Figure 5 shows all operational graphs in the three modes of operation defined above. F. Performance indicators Performance indicators are designed to measure developed supervisor effectiveness. Following management objectives, we propose two indicators respectively dedicated to measure planned production satisfaction and frequency support efficiency. Indicator of program production satisfaction: This indicator is based on the MAPE score evaluated each 3 minutes with the formula: n M = n E inj E pg t= E pg E inj : injected 3 min energy to the grid and E pg planned production. Indicator of frequency changes: This indicator is based on calculating the average of frequency change in 5 minutes: T Δf T t= C_t. () 4

5 Fig.3. Block diagrams of the studied system 5

6 MN LMN ΔP bat Z LMP MP HP MN LMN P bat_ref Z LMP MP HP ΔP SdP MN LMN Z LMP MP HP P SdP_ref MN LMN Z LMP MP HP Low SoC bat Medium High N Δp bat_ref Z P HP Low SoC SdP Medium High N Δp sc_ref Z P HP N Δf bat Z P HP Kpv N Δf SdP Z P HP Notations: H : High M: Medium L : Low Z : Zero N : Négative P : Positive Fig.4. Membership functions associated with the direct various inputs and outputs of the supervisor 6

7 Fig.5. Operational grpahs 7

8 3. Principle of batteries deterioration calculating To quantify the total damage produced by different storage elements charge-discharges cycles, we use the cumulative law proposed by Miner [3-4]. It is defined as follows: N D = n i N i i= Where n i : cycles number at amplitude DoD i. N i : Lifespan cycles at amplitude DoD i. Thus, this factor allows us to assess storage elements lifespan for a degradation D equal to 8%. Figure 6 shows the principle of batteries cumulative degradation calculation. (2) Fig. 6. Principle of batteries deterioration calculating 4. Results This section is divided into two parts whose objectives are: - developed supervision strategy validation by simulation; - showing the contribution of storage hybridization on batteries durability. Simulations are done according to these powers: - Photovoltaic source peak power: 3 kw. - Nominal NCA lithium batteries power (energy storage source): 6 kw. - Nominal power for storage power source (super-capacitors): 5kW. Scenarios of photovoltaic generated and planned power correspond to forecasts at Day + in a real site for year 23. These data are collected on an hourly average. It should be noted that photovoltaic production profiles are modified to incorporate effects of sudden weather changes. A. Supervisor validation Figure 7 shows the evolution of storage power to meet production schedule. Figure 8 distinguishes the response of the two storage units. Figure 9 shows the percentage of error on production program satisfaction (MAPE score evaluated every 3 minutes). It remains below.8% for studied case. Figure shows that network support function reduces frequency variations amplitude. B. Storage elements lifespan assessment According to results found by damage accumulation method (Table 2), it is clear that the addition of a source of power storage (super-capacitors) allows increasing the longevity of lithium batteries NCA (source of energy). 8

9 2.5 x 4 2 P-pg P-pv Pst-ref 2 8 Pbat-ref Psc-ref P (W).5.5 Powerws (W) Time(h) Fig. 7. Evolution of PV power plant powers on a day Time (h) Fig. 8. Complementary operation between NCA lithium batteries and super-capacitors power storage source. Mape Score % (3mn step) Time (h) Fig. 9. Percentage of error on production program satisfaction (MAPE score evaluated every 3 minutes). f(hz) Time (h) Fig.. Evolution of frequency every 5 minutes. Table II: Comparison of storage elements Lifespan for different without and with super-capacitors Only energy storage source Hybrid storage (Energy power sources) and Batteries Lithium NCA (6 kw) Lifetime (years) Lifetime (number of cycles) Energy source : Batteries Lithium NCA (6 kw) Power source : SuperCaps Maxwells (5kW) Batteries NCA 6 kw > 25 > 6 for a DoD of 8% [3] Conclusion This work concerns the development of a management algorithm for a photovoltaic system that combines two storage technologies (NCA Lithium batteries and super-capacitors). Simulations show that developed algorithm achieves the desired objectives in terms of compliance with production program while respecting the various constraints of network manager. Furthermore, analysis of batteries lifespan proves that combination of batteries with super-capacitors enables to increase their longevity. 9

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